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231562a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 | import os
import json
import pdb
import numpy as np
from collections import defaultdict
import argparse
gpt_name = {
'gpt3.5': 'text-davinci-003',
'gpt3.5-chat': 'gpt-3.5-turbo',
'gpt4': 'gpt-4',
}
parser = argparse.ArgumentParser(prog='Layout Evaluation', description='Layout evaluation for counting prompts.')
parser.add_argument('--input_info_dir', type=str, default='./dataset/NSR-1K/counting/')
parser.add_argument("-f", "--file", type=str)
parser.add_argument('--score_dir', type=str, default='./eval_score/counting/')
parser.add_argument('--setting', type=str, default='counting', choices=['counting', 'counting.single_category', 'counting.two_categories', 'counting.reasoning', 'counting.mscoco'])
parser.add_argument('--verbose', default=False, action='store_true')
args = parser.parse_args()
def _main(args):
# load prediction results
# pred_filename = f'{args.gpt_type}.{args.setting}.{args.icl_type}.k_{args.K}.px_{args.canvas_size}.json'
# prediction_list = json.load(open(os.path.join(args.prediction_dir, pred_filename)))
pred_filename = os.path.basename(args.file)
prediction_list = json.load(open(args.file))
# load gt val examples
val_example_files = os.path.join(
args.input_info_dir,
f'{args.setting}.val.json',
)
val_example_list = json.load(open(val_example_files))
id2subtype = {d['id']:d['sub-type'] for d in val_example_list}
ref_file = {x['id']: x for x in val_example_list}
precision_list = []
recall_list = []
iou_list = []
mae_list = []
acc_list = []
for pred_eg in prediction_list:
val_eg = ref_file[int(pred_eg['query_id'])]
pred_object_count = defaultdict(lambda: 0)
for category, _ in pred_eg['object_list']:
if category is None: continue
for x in val_eg['num_object']:
if category.lstrip("a ").lstrip("an ").lstrip("the ") in x[0] or x[0] in category.lstrip("a ").lstrip("an ").lstrip("the "):
category = x[0]
pred_object_count[category] += 1
if id2subtype[pred_eg['query_id']] == 'comparison':
(obj1, gt_num1), (obj2, gt_num2) = val_eg['num_object']
pred_num1 = pred_object_count[obj1]
pred_num2 = pred_object_count[obj2]
# equal cases
if gt_num1 == gt_num2 == pred_num1 == pred_num2:
acc_list.append(1)
# < or >
elif gt_num1 == pred_num1 and (gt_num1-gt_num2)*(pred_num1-pred_num2) > 0:
acc_list.append(1)
else:
acc_list.append(0)
else:
cnt_gt_total = 0
cnt_pred_total = sum(pred_object_count.values())
cnt_intersection_total = 0
cnt_union_total = 0
absolute_error = 0
appeared_category_list = []
all_matched = True
for category, gt_cnt in val_eg['num_object']:
cnt_gt_total += gt_cnt
pred_cnt = pred_object_count[category]
cnt_intersection_total += min(pred_cnt, gt_cnt)
cnt_union_total += max(pred_cnt, gt_cnt)
absolute_error += abs(pred_cnt - gt_cnt)
appeared_category_list.append(category)
if pred_cnt != gt_cnt: # check if all the mentioned objects are predicted correctly
all_matched = False
# accuracy
acc_list.append(1 if all_matched else 0)
# MAE
if not len(appeared_category_list):
mae_list.append(0)
else:
mae_list.append(float(absolute_error) / len(appeared_category_list))
# precision, recall, IoU
if not cnt_intersection_total:
precision_list.append(0)
recall_list.append(0)
iou_list.append(0)
else:
precision_list.append(float(cnt_intersection_total) / cnt_pred_total)
recall_list.append(float(cnt_intersection_total) / cnt_gt_total)
iou_list.append(float(cnt_intersection_total) / cnt_union_total)
# print results
# print(f'Setting: {args.setting} (#eg: {len(prediction_list)})\tGPT-3: {args.gpt_type} - {args.icl_type}\tk = {args.K}')
print(f'{pred_filename}, #eg: {len(prediction_list)}')
avg_precision = np.mean(precision_list)
avg_recall = np.mean(recall_list)
avg_acc = np.mean(acc_list)
avg_mae = np.mean(mae_list)
avg_iou = np.mean(iou_list)
score_info = {
'precision': avg_precision,
'recall': avg_recall,
'precision_list': precision_list,
'recall_list': recall_list,
'acc': avg_acc,
'acc_list': acc_list,
'mae': avg_mae,
'mae_list': mae_list,
'iou': avg_iou,
'iou_list': iou_list,
}
print(f'\tPrecision = {avg_precision*100:.2f} %\n\tRecall = {avg_recall*100:.2f} %' \
f'\n\tIoU = {avg_iou*100:.2f} %\n\tMAE = {avg_mae:.2f}\n\tacc = {avg_acc*100:.2f} %')
# save output
os.makedirs(args.score_dir, exist_ok=True)
output_filename = os.path.join(args.score_dir, f'layout_eval.{pred_filename}')
with open(output_filename, 'w') as fout:
json.dump(score_info, fout)
return score_info
if __name__ == '__main__':
_main(args) |